ASSESSMENT AND PREDICTION OF LAND SURFACE TEMPERATURE (LST) OF DAMATURU METROPOLIS FROM 2022-2042

The mounting issue of urban heat and climate warming within cities has become a pressing concern, with Damaturu being no exception. Land surface temperature (LST) estimation and prediction play a crucial role in comprehending climate dynamics and formulating effective mitigation and adaptation strategies. This paper presents an analysis of randomly selected Landsat satellite images spanning from 2013 to 2022. Rigorous image correction techniques and spatial data analysis were employed to extract temperature values of Damaturu metropolis during this period, utilizing ArcGIS 10.8 and QGIS 3.10.6 software. Consequently, using the forecast tool and the Artificial Neural Network model in SPSS, future trends of Land surface Temperature in Damaturu metropolis from 2022 to 2042 were projected, leveraging the model’s demonstrated efficacy in utilizing past trends to predict future events. The performance and implications of the employed prediction model were assessed by examining various indicators. The findings reveal a gradual increase in LST starting from 2027, raising concerns regarding potential temperature amplification and its impact on local ecosystems, human health, and socio-economic activities. This underscores the significance of ongoing afforestation programs and the urgent need for enhanced climate change adaptation strategies. However, the findings also indicate the potential impact of afforestation programs, specifically the Yobe State tree plantations, on temperature stability between 2016 and 2022. The study contributes to a better understanding of the relationship between afforestation initiatives and temperature dynamics, providing valuable insights for climate change adaptation strategies. Furthermore, this study will support the state government in achieving SDG 13 target 2, which entails integrating climate change measures into national policies, strategies, and planning. While the forecasts offer valuable insights, it is important to acknowledge the inherent uncertainties in climate modelling, emphasizing the need for flexibility and adaptation in policymaking processes.